Pre-visualization of the normalized count data before any differential analysis
Table of content
Generated from a Jupyter Notebook - Sources
List of genes to check: Egr1, Jun, Zfp36l1, Malat1, Dusp1, Nr4a1, Fos
Normalized counts
Z-scores
Genes in list, but not in the counts
Normalized counts
Z-score
Genes that could be artefact due to FACS sorting or contamination from other cell types and samples that seem contaminated: 'GF_8w_M_2_2', 'SPF_52w_F_1_2', 'SPF_104w_M_3_2'
Filter the normalized counts (value before and )
Filter the samples from annotations and metadata
Clustering method: Ward D2
Extract genes from chrX / chrX_GL456233_random / chrY
Remove genes (number displayed) from the count table
Clustering method: Ward D2
Male & Young
Male & Middle-aged
Male & Old
Female & Young
Female & Middle-aged
Female & Old
Column order: microbiota - sex - age
Column order: microbiota - sex - age
Weighted gene co-expression network analysis using WGCNA package
Keep only genes that have a count >= 10 in more than 90% of the samples (number removed / kept displayed)
Analysis of scale free topology for multiple soft thresholding powers, with signed hybrid network type
Block-wise network construction and module detection
Size of the modules (ME0: genes not assigned to a module) and number of genes in modules
Dendrogram and the module colors underneath the block
Associate module color to genes
Recalculate MEs with color labels
Extract the palette for next plots
Module-trait correlation analysis between the module eigengene (ME) and the different trait (combination of Microbiota, age and sex)
Associate genes to modules
The mean of the Z-score over the samples in the group is plot for each gene